U.S. patent number 10,489,793 [Application Number 15/452,511] was granted by the patent office on 2019-11-26 for system and method for estimating co2 emission.
This patent grant is currently assigned to Refinitiv US Organization LLC. The grantee listed for this patent is Refinitiv US Organization LLC. Invention is credited to Christopher Greenwald, Peter Ohnemus, Peter Pircher.
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United States Patent |
10,489,793 |
Ohnemus , et al. |
November 26, 2019 |
System and method for estimating CO2 emission
Abstract
Estimations of carbon dioxide ("CO2") emission of an entity upon
the condition of incomplete or missing data uses one or more
algorithms implemented in a machine having a processor and a memory
and data concerning the entity. The data is applied to an algorithm
implemented as code executable in the processor. The algorithm
produces a result that comprises an estimate of the CO2 emission of
the entity. The CO2 emission estimate can be output to a user, and
the underlying formula and data can inspected and optionally
modified by users with suitable permissions. The CO2 emission
estimate can be applied as a factor in a formula to compute a
rating for the entity which can be output from the machine. Error
estimates associated with the data used by the algorithm can be
generated to provide improved estimates.
Inventors: |
Ohnemus; Peter (Herrliberg,
CH), Greenwald; Christopher (Baden, CH),
Pircher; Peter (Baar, CH) |
Applicant: |
Name |
City |
State |
Country |
Type |
Refinitiv US Organization LLC |
New York |
NY |
US |
|
|
Assignee: |
Refinitiv US Organization LLC
(New York, NY)
|
Family
ID: |
42560759 |
Appl.
No.: |
15/452,511 |
Filed: |
March 7, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170178147 A1 |
Jun 22, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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14060242 |
Oct 22, 2013 |
9613331 |
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13629186 |
Sep 27, 2012 |
8645151 |
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12702593 |
Feb 9, 2010 |
8321234 |
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61152591 |
Feb 13, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
10/10 (20130101); G06Q 10/067 (20130101); G06Q
30/018 (20130101); G06Q 50/18 (20130101); G06Q
10/06393 (20130101); G06Q 30/0282 (20130101); G06Q
10/06 (20130101) |
Current International
Class: |
G06Q
10/10 (20120101); G06Q 10/06 (20120101); G06Q
30/06 (20120101); G06Q 30/02 (20120101); G06Q
10/08 (20120101); G06Q 30/00 (20120101); G06Q
50/18 (20120101) |
Field of
Search: |
;705/1.1-912 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Ouellette; Jonathan P
Attorney, Agent or Firm: Norton Rose Fulbright US LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application is a continuation of U.S. patent
application Ser. No. 14/060,242, filed Oct. 22, 2013 and entitled
"System And Method For Estimating Rating Criteria Values," which is
a continuation of U.S. patent application Ser. No. 13/629,186 filed
Sep. 27, 2012 entitled "CO.sub.2 Estimator Module " now U.S. Pat.
No. 8,645,151, which is a continuation of U.S. patent application
Ser. No. 12/702,593, filed Feb. 9, 2010, entitled "System and
Method for Estimating CO.sub.2 Emissions," now U.S. Pat. No.
8,321,234, which claims the benefit of U.S. patent application Ser.
No. 61/152,591, filed Feb. 13, 2009 and entitled "System Including
CO.sub.2 Emission Estimator Module," all of which are hereby
incorporated by reference in their respective entireties.
Claims
We claim:
1. A method comprising: computing, by at least one estimation
module of a machine having a processor and a memory, using
incomplete data relating to an environmental factor for an entity,
at least one component of an estimate of the environmental factor,
wherein the computing is performed by applying a heuristic model
algorithm associated with the at least one estimation module to the
incomplete data, wherein the at least one estimation module is one
of a plurality of estimation modules, each estimation module of the
plurality of estimation modules being associated with a respective
model algorithm for computing a respective component of the at
least one component of the estimate; generating, by the processor,
a rating of the entity based on the estimate, wherein the
generating includes applying the estimate as a factor in a formula
to generate the rating for the entity; and outputting the rating
from the machine.
2. A method as in claim 1, further comprising: receiving at the
machine data relating to an industry, sector, or sub-sector to
which the entity belongs, wherein the data relating to the
industry, sector, or sub-sector includes the data relating to the
environmental factor; determining by the processor one or more
circumstances in which the data relating to the environmental
factor for the entity is at least one of missing and incomplete,
wherein the at least one estimation module includes a plurality of
estimation modules, and wherein each estimation module of the
plurality of estimation modules encodes a respective heuristic
model algorithm; and computing, by each estimation module of the
plurality of estimation modules, using the respective heuristic
model algorithm, a different component of the estimate of the
environmental factor of the entity.
3. A method as in claim 2, wherein the step of computing, by each
estimation module of the plurality of estimation modules, the
different component of the estimate of the environmental factor of
the entity is performed in parallel.
4. A method as in claim 2, further comprising combining the results
of the plurality of estimation modules.
5. A method as in claim 4, wherein the combining the results of the
plurality of estimation modules includes computing a simple average
that is provided as the environmental factor in the form of a
single value.
6. A method as in claim 2, wherein the respective heuristic model
algorithms have a hierarchical accuracy order, and wherein the
computing, by each estimation module of the plurality of estimation
modules, of the different component of the estimate is performed in
accordance with the hierarchical accuracy order.
7. A method as in claim 2, including the additional steps of:
computing an error associated with each respective heuristic model
algorithm by applying each respective heuristic model algorithm to
at least one additional entity having a known value concerning the
environmental factor; and calculating a difference between the
estimate for the additional entity concerning the environmental
factor and the known value.
8. A method as in claim 7, including the additional step of
correcting for any calculated difference concerning the additional
entity so as to account for such error in the estimate for the
entity.
9. A method as in claim 1, further comprising making the estimate
available to a user through a user interface.
10. A method as in claim 9, wherein the making step is selectively
enabled based upon operation of a data-fault module executing in
the machine, the data-fault module being operative to configure the
processor to enable the making step if the entity is determined to
have missing or incomplete data concerning the environmental
factor.
11. A method as in claim 1, further comprising a threshold step of
determining, via a data-fault module executing in the machine,
whether the entity has missing or incomplete data relating to the
environmental factor for the entity, and performing the remaining
steps if the entity is determined to have missing or incomplete
data relating to the environmental factor for the entity.
12. A method as in claim 2, including the additional steps of:
providing a user interface to the machine; and permitting users,
through the user-interface, to customize at least one of the
respective heuristic model algorithms.
13. A method as in claim 1, wherein the entity is selected from the
group consisting essentially of a company, a corporation, a limited
liability company, a limited liability corporation, a partnership,
a limited liability partnership, a self-regulated organization, and
a joint venture.
14. A method comprising: computing, by at least one estimation
module of a machine having a processor and a memory, using
incomplete data relating to an environmental factor for an entity,
at least one component of an estimate of the environmental factor,
wherein the computing is performed by applying a heuristic model
algorithm associated with the at least one estimation module to the
incomplete data, wherein the at least one estimation module is one
of a plurality of estimation modules, each estimation module of the
plurality of estimation modules being associated with a respective
model algorithm for computing a respective component of the at
least one component of the estimate; estimating by the processor
the environmental factor of the entity based on the at least one
component of the estimate computed by the at least one estimation
module; and outputting the estimate from the machine.
15. A method as in claim 14, further comprising: receiving at the
machine data relating to an industry, sector, or sub-sector to
which the entity belongs, wherein the data relating to the
industry, sector, or sub-sector includes the data relating to the
environmental factor; determining by the processor one or more
circumstances in which the data relating to the environmental
factor for the entity is at least one of missing and incomplete,
wherein the at least one estimation module includes a plurality of
estimation modules, and wherein each estimation module of the
plurality of estimation modules encodes a respective heuristic
model algorithm; and computing, by each estimation module of the
plurality of estimation modules, using the respective heuristic
model algorithm, a different component of the estimate of the
environmental factor of the entity.
16. A method as in claim 15, wherein the step of computing, by each
estimation module of the plurality of estimation modules, the
different component of the estimate of the environmental factor of
the entity is performed in parallel.
17. A method as in claim 15, further comprising combining the
results of the plurality of estimation modules, wherein the
combining the results of the plurality of estimation modules
includes computing a simple average that is provided as the
environmental factor in the form of a single value.
18. A method as in claim 15, including the additional steps of:
computing an error associated with each respective heuristic model
algorithm by applying each respective heuristic model algorithm to
at least one additional entity having a known value concerning the
environmental factor; calculating a difference between the estimate
for the additional entity concerning the environmental factor and
the known value; and correcting for any calculated difference
concerning the additional entity so as to account for such error in
the estimate for the entity.
19. A method as in claim 14, further comprising making the estimate
available to a user through a user interface.
20. A method as in claim 19, wherein the making step is selectively
enabled based upon operation of a data-fault module executing in
the machine, the data-fault module being operative to configure the
processor to enable the making step if the entity is determined to
have missing or incomplete data concerning the environmental
factor.
Description
FIELD OF THE INVENTION
The present invention concerns the rating and benchmarking of
companies using extrafinancial information such as environmental
data. More particularly, carbon dioxide emission data is estimated
using a programmed machine and automatically presented through an
interface in the absence of data on this topic for the current
reporting period or when complete information is not available.
BACKGROUND OF THE INVENTION
Depending on the sector and the company, CO2 emission data may be
available in a timely manner, or may not be available at all.
Across a spectrum of companies, the range of information disclosed
and available through corporate documents and to news reporting
services can vary from thorough to partial to none. When such data
is available, it can be compiled (e.g., its components can be
weighted) and presented as a value that is useful in rating a
company relative to its peers. U.S. Pat. No. 7,277,864, assigned to
the present assignee, describes rating systems that can accommodate
such extra-financial data, when present. However, improvements are
needed in the art to address circumstances that are all too common
in which incomplete or non-existent data provides no value for
rating an entity such as a company. The present invention addresses
this problem using heuristics that provide multiple models for
estimating CO2 total emission and for providing, automatically, an
estimated value when a reported value is not available.
SUMMARY OF THE INVENTION
In accordance with one aspect of the invention, a method for
outputting a rating based upon an estimate of a carbon dioxide
emission of an entity is implemented in a machine having a
processor and a memory. According to this aspect, data is received
at the machine-concerning the entity. The data is applied to an
algorithm implemented as code executable in the processor. The
algorithm produces a result that comprises an estimate of the
carbon dioxide emission of the entity. The carbon dioxide emission
estimate is applied as a factor in a formula to compute a rating
for the entity. The rating is output from the machine.
In accordance with another aspect of the invention, a method for
estimating a carbon dioxide emission of an entity is implemented in
a machine having a processor and a memory. In this method, data as
described above is received at the machine and applied to a
code-implemented algorithm that produces an estimate of the carbon
dioxide emission of the entity. The estimated carbon dioxide
emission is then output from the machine.
Methods in accordance with further, optional aspects of the
invention can have the algorithm apply data concerning an industry,
sector, or sub-sector to which the entity belongs. Also, methods in
accordance with further optional aspects can have the data that is
applied to the algorithm include both qualitative and quantitative
data. Also, optionally, the estimated carbon dioxide emission can
be made available to a user through a user interface, and, if so,
can be a result of operation of a data-fault module executing in
the machine, that makes the estimated carbon dioxide emission value
available if the entity is determined to have missing or incomplete
carbon dioxide emission data. In addition, more than one algorithm
can be applied to the data so as to provide discrete estimates of
the carbon dioxide emission from each algorithm that are combined
with one another, such as, by way of example, a simple average.
Another, optional feature of a method in accordance with the
invention can include the additional steps of computing an error
associated with the algorithm(s) by applying the algorithm(s) to at
least one additional entity having a known carbon dioxide emission,
and calculating a difference between the estimated carbon dioxide
emission for the additional entity and the known carbon dioxide
emission. Any calculated difference can be used to correct for
error in the estimated carbon dioxide emission for the entity.
In a further aspect, methods as described above can be further
arranged to permit users, through a user-interface, to customize
the algorithm(s).
In still a further aspect of the invention, a computer-implemented
system combines plural modules that cooperate to output a carbon
dioxide emission estimate in the absence of data or when complete
data is not available for a given period (e.g., year). The carbon
dioxide emission estimate can be calculated in connection with the
operation of a rating program or system and utilized to supply a
value or values that are used in the calculation of a rating for an
entity and so on.
These and other features, aspects and advantages of the invention
can be appreciated from the following Description of Certain
Embodiments of the Invention and the accompanying Drawing
Figures.
DESCRIPTION OF THE DRAWING FIGURES
FIG. 1 is a schematic diagram illustrating at a high-level certain
variable types that can be used in estimating carbon production by
an entity.
FIG. 2 is a chart showing a general trend of sectors with large
carbon emissions having better reporting that sectors with
comparatively lower carbon emissions, and further illustrates, for
calendar year 2006 data, the percentage of companies reporting by
sector.
FIG. 3 is block diagram of a computer system 300 configured far
employment of a method in accordance with one embodiment of the
invention.
FIG. 4 is a block diagram showing a set of modules that cooperate
to selectively provide a carbon dioxide emission estimate to a user
or to a rating program.
FIGS. 5A, 5B and 5C show a portion of a user-interface associated
with a rating program as may be seen by a user.
FIG. 6, shows a portion of a user interface that presents details
concerning a parameter of a main company under investigation
including its value and the basis for its calculation.
FIG. 7 can be part of the user interface of FIG. 6 or a separate
page selectable by a user, and provides a link and textual support
for the value of the data point.
FIG. 8 is a detail view of a further aspect of the user interface,
showing ratings at various hierarchical levels starting with a
category and delving deeper down to data points of emission
reduction (which in this example is an "A" rating), and provides a
of a particular outcome, all shown concurrently on the same
user-interface display.
DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION
By way of overview and introduction, the total CO2 emission of an
entity can be calculated in accordance with a broad aspect of the
invention by combining discrete estimates into a total estimate,
wherein each discrete estimate concerns a portion of the total
estimate and uses a common pool of data concerning the entity, and
one of its industry, sub-sector or sector. In one embodiment,
carbon estimates are made using four models, if underlying data is
available to drive the algorithms used by these models, and no less
than two models. Optionally, additional models can be used.
The following discussion is made in regard to the entity being a
company of one sort or another, but it will be appreciated that the
invention applies to entities of a variety of structures, including
without limitation, companies, corporations, limited liability
companies, limited liability corporations, partnerships, limited
liability partnerships, self-regulated organizations, and joint
ventures.
The estimates are based on variables that concern the company and
an industry, sector, or sub-sector to which the company belongs.
The can be both quantitative and qualitative. As shown in FIG. 1,
certain variable types that can be used in estimating carbon
production by a company, including geographic factors, emissions of
various types, energy consumption by the company, the number of
employees, sales data for the company and company policies. Each of
these data types provide indirect information that are combined by
an algorithm, in accordance with the invention, to arrive at
several carbon dioxide emission estimates that can be further
combined into a total carbon dioxide emission estimate for the
company.
The estimates are required to properly assess an investment risk in
many companies due to incomplete reporting by many companies in
many sectors. In some cases, there is no reporting at all which
exposes investors and portfolio managers to risks based on
uncertainties. In FIG. 2, a chart shows a trend across several
sectors (based on data collected for the 2006 calendar year) in
which sectors that have large carbon emissions generally have
better reporting that sectors with comparatively lower carbon
emissions. What can be appreciated, however, is that no sector is
complete in reporting its emissions data. The utility sector has
the best performance in this regard, at about 70% reporting whereas
companies that make consumer discretionary products have the lowest
reporting performance.
In order to rate a company on a reasoned basis, data has to be
used, whether it is quantitative or qualitative. Incomplete or
missing data causes difficulties in comparing companies on a fair
basis to each other, to their sector, and so on. One approach has
been to assume that if a company is not reporting the data, that
the data is bad, but that can is skew a rating calculation and
distort a rating that otherwise might have been good. This is
particularly true now that the environmental impact of a company is
part of an assessment by many investors and managers.
Referring briefly now to FIG. 3, a block diagram illustrates a
computer system 300 configured for employment of the methods
described herein. System 300 includes a user interface 305, a
processor 310, and a memory 315. System 300 may be implemented on a
general purpose microcomputer, such as one of the members of the
Sun.RTM. Microsystems family of computer systems, one of the
members of the IBM.RTM. Personal Computer family, one of the
members of the Apple.RTM. Computer family, or a myriad other
conventional workstation, desktop computer, laptop computer,
netbook computer, personal digital assistant, and smart phone
devices that are generally available in the marketplace. Although
system 300 is represented herein as a standalone system, it is not
limited to such, but instead can be coupled to other computer
systems via a network (not shown).
Memory 315 is a memory for storing data and instructions suitable
for controlling the operation of processor 310. An implementation
of memory 315 would include a random access memory (RAM), a hard
drive and a read only memory (ROM). One of the components stored in
memory 315 is a program 320.
Program 320 includes instructions for controlling processor 310 to
execute the methods described herein. Program 320 may be
implemented as a single module 322 or as a plurality of modules
322A, 322B, . . . 322N (where N is an arbitrary integer) that
operate in cooperation with one another. Program 320 is
contemplated as representing a software embodiment of the method
described hereinabove.
User interface 305 includes an input device, such as a keyboard,
touch screen, tablet, or speech recognition subsystem, for enabling
a user to communicate information and command selections to
processor 310. User interface 305 also includes an output device
such as a display or a printer. In the case of a touch screen, the
input and output functions are provided by the same structure. A
cursor control such as a mouse, track-ball, or joy stick, allows
the user to manipulate a cursor on the display for communicating
additional information and command selections to processor 310.
While program 320 is indicated as already loaded into memory 315,
it may be configured on a storage media 325 for subsequent loading
into memory 315. Storage media 325 can be any conventional storage
media such as a magnetic tape, an optical storage media, a compact
disc, or a floppy disc. Alternatively, storage media 325 can be a
random access memory, or other type of electronic storage, located
on a remote storage system.
In operation, the program 320 can take inputs from the memory 315
or from a remote data source. Also, the program can generate
outputs through the user interface 305 or to a remote location via
a communication port 340. The communication port can exchange
messages and data using any number of conventional data transfer
schemes and protocols.
Referring now to FIG. 4, a system configuration 400 is illustrated
in which a plurality of algorithms are each implemented as separate
modules 322A, 322B, 322C, and 322D (more generally referred to
hereinbelow as modules 322 unless specifics of a particular module
are being described), with each module comprising code that
executes in the processor 310. The modules 322 are each coupled to
one or more data sources across a distributed computer network 410.
The various data sources are schematically represented as a single
database 420, but it is to be understood that the data can be
collected and stored in a single database, or can reside in
multiple places that are accessible by the distributed computer
network 410.
The modules thus encode heuristics in accordance with different
models that can estimate carbon dioxide emissions, and each
comprises code in the memory 315 of the machine that has the
processor 310. The modules singularly or collectively implement one
or more algorithms that transform data received from the data
source 420 into an estimate of the carbon dioxide emission of the
company under investigation (the "main-company"). A CO2 estimator
module 324 includes code for all of the models, though each model
can be a separate module 322A-D (as illustrated). The processor
executes the code to cause each model to compute an estimate. Each
model computes a different component of a total CO2 emission
estimate that can be output from the system 300 to a user machine
430 via a communicative connection 440 (e.g., as part of the
content of a webpage or other file communicated to the
user-machine. For example, a further module can comprise
instructions executing in the processor that cause data to be
transferred across the communication port 340 so as to output the
rating from the machine. The co2 estimator module provides the
results as a combined (e.g., a simple average), single output
value. Thus, if the models comprise individual modules, the co2
estimator receives their respective outputs and processes that data
in order to provide a single output value.
In a preferred mode, the CO2 estimator module 324 operates to
complement the operation and output of a rating program 326 (which
can be a second module that is part of or distinct from the modules
322), such as described in the aforementioned U.S. Pat. No.
7,277,864, which is hereby incorporated by reference as if set
forth in its entirety herein. The value(s) output by the CO2
estimator module 324 can be provided to the rating program 326 via
a connection 450. Meanwhile, a data-fault module 328 operates as
well to determine circumstances in which a particular company is
missing or has incomplete CO2 emission data. The data-fault module
causes the single output value of the co2 estimator module 324 to
be presented to the user through a user interface at the
user-machine 430 when there is missing or incomplete CO2 emission
data. Otherwise, the data-fault module will not present estimated
values through the interface, and, instead, the reported value for
CO2 emission data for the company will be presented. As such, the
rating program 326 can identify to the data-fault module 328
situations in which there is missing or incomplete CO2 emission
data such that a rating value for a given parameter cannot be
calculated. The data-fault module 328 can thereafter instruct the
CO2 estimator 324 to provide or both compute and provide an
estimated carbon dioxide emission value to the rating program over
line 450. The rating program 326 can then compute a rating of the
company using the value supplied by the CO2 estimator 324 rather
than make assumptions or fail due to the absence of or incomplete
data.
It should be appreciated that the data-fault module 328 can be
incorporated into the rating program 326, and that the CO2
estimator 324 and any associated modules can be incorporated in the
rating program as variations within the scope of a code-driven
embodiment
With further reference now to the modules in FIG. 4, the CO2
estimator 324 is illustrated as having four models encoded in
respective modules: a "CO2 model" within module 322A, an "Energy
model" within module 322B, a "Median model" within module 322C, and
a "Policy model" within module 322D. Each model returns one
estimate, making a total of four estimate numbers provided to the
CO2 estimator 324. Not all are expected to provide the same
accuracy, and the following order should be but is not required to
be respected. In particular, the "CO2" model should be used if
available as it provides "derived data." Otherwise, and if
available, the "Energy" model should be used as it also provides
"derived data." Next, otherwise, the "Median" model should be used
as it provides "estimated data," potentially together with the
"Policy model" as it provides an "adjustment."
Any error associated with a particular model can be estimated by
taking, for example, ten companies for which the data source 420
provides the total CO2 emissions, and by then applying each
model/module 322 as if the total CO2 emissions were not known. The
difference between the estimate and the reported number provides an
idea of the error associated to the model. Preferably, the
benchmarks used for computing the error are for companies having
known total CO2 emissions that are in the same industry, sector or
subsector as the company whose CO2 emission is being estimated.
Module 322A includes instructions that implement a "CO2 model." The
heuristics of this module are configured to provide a value that is
part of the estimate for the total CO2 emissions of a company of
interest. In particular, the instructions obtain, such as by a
query of the data source 420 via the communication port 340, the
latest available total CO2 emission for the company. For instance,
this may be from the previous calendar year, or from years ago,
etc. This value is divided by the number of employees that the
company had during the same year as the latest available total CO2
emission number. This provides a normalized value of emissions per
employee. Next, the normalized value is multiplied by the number of
employees of the company during the year in which we are
calculating the CO2 emission estimate. In similar manner, the total
CO2 emission number for the company is divided by the net sales
figure reported by that company, in original currency, to provide a
normalized value of emissions per net sales which is then
multiplied by the same net sales figure of the company for the year
in which we are calculating the CO2 emission estimate. The estimate
provided from this heuristic approach can be the average of the two
numbers calculated as the CO2 emission estimate, or can comprise
one of these two numbers if the other cannot be calculated (e.g.,
if employee numbers are not reported, then the estimate provided by
this module can comprise the number calculated using only net sales
data).
By the CO2 model, the working assumption is that the magnitude of
sales or employees bears a direct relationship to the CO2 emission
value, such that a new estimate for CO2 emission in a given year
can be "derived" from a known value in a prior year.
Module 322B includes instructions that implement an "energy model."
The heuristics of this module are configured to provide a value
that is part of the estimate for the total CO2 emissions of a
company of interest, and like module 322A, derives an estimate from
known values in prior years. In particular, the instructions
obtain, such as by a query of the data source 420 via the
communication port 340, the latest available total energy purchase
by the company in support of its operations. For instance, this may
be from the previous calendar year, or from years ago, etc. Note
that for companies in the utility sector, the total energy sold is
the metric to identify and use. This value is divided by the number
of employees that the company had during the same year as the
latest available total energy purchase number. This provides a
normalized value of total energy purchased (or sold, in the case of
utilities) per employee for the main-company whose CO2 emission is
to be estimated. Next, the same normalized values (ratios) are
computed for all the other companies in the same industry (e.g.,
using 6 GICS digits) as the main-company, using total energy
purchase data obtained from the data source 420, as described
above. If the number of available ratios is smaller than 10, then
the set of companies should be extended to the sub-sector (4 GICS
digits). If the number of available ratios is still smaller than
10, then the set of companies should be extended to the sector (2
GICS digits). The reason for this is to provide a set of ratios for
use in the next algorithmic step.
The algorithm continues by computing the percentile rank of the
normalized value for the main-company within and among the ratios
for the other companies whose normalized values were computed.
(i.e., for the other companies in the same industry, sector or
subsector), referred to herein as the "comparison class." Each
member of the comparison class must have a known total CO2 emission
for the latest available year being analyzed for the main-company,
as will be appreciated from the subsequent steps performed by the
algorithm. In any event, as a result of this step, the algorithm
identifies a rank-position of the main company relative to the
comparison class.
Next, the total CO2 emissions output of the comparison class
members is divided by the number of employees to obtain a CO2
emission per employee value for the companies in the comparison
class. These results are ranked and the rank-position of the
main-company is used to select a CO2 emission per employee value of
the company that matches the main-company's rank-position that was
calculated in terms of energy purchased (or sold). This figure is
multiplied by the number of employees of the main-company to
provide a first (or only) component in the CO2 estimate output by
this module 322B.
The foregoing steps can then be repeated using net sales, in
original currency, instead of number of employees to Arrive at a
similar figure that is defined in terms of net sales rather than in
terms per employee. Specifically, the total energy purchased by the
main-company is divided by the net sales number for the latest-year
having available data. The same metric is computed for each member
of the comparison class. The main-company's rank-position is
determined in terms of its total energy usage, and then that
rank-position is used to select a company in the comparison class
that has been ranked in terms of its CO2 emission per employee. The
company so-selected has its CO2 emission per employee multiplied by
the number of employees of the main-company to provide a second (or
only) component in the CO2 estimate output by this module 322B.
The estimate output by the module 322B using the energy model can
be the average of the components mentioned above, or can be the
value of either component, but in either case is a derived value
based on the assumption that energy purchases by a company can be
correlated in comparison to companies in a comparison class with
the CO2 emission amount of companies in the comparison class, such
that a new estimate for CO2 emission in a given year can be
"derived" from a known value of a comparison-class company in a
prior year.
Module 322C includes instructions that implement a "median model."
The heuristics of this module are configured to provide a value
that is part of the estimate for the total CO2 emissions of a
company of interest. In particular, the instructions obtain, such
as by a query of the data source 420 via the communication port
340, of the total CO2 emission for the comparison class to the
main-company for the same year that we are to compute a total CO2
estimate for the main-company. This data for each of the
comparison-class members is divided by the number of employees of
such companies (also obtained from the data source 420) to obtain a
CO2 emission per employee value for the comparison-class companies.
The algorithm then computes a median value for the comparison
class. The median value is multiplied by the number of employees of
the main-company for the same year in question to provide a first
(or only) component in the CO2 estimate output by this module 322C.
For the same year in question, the CO2 emissions for the comparison
class is divided by their respective net sales (in original
currency; obtained from the data source 420) to obtain a CO2
emission per net sales value for the comparison-class companies.
The algorithm then computes a median value for the comparison class
using this metric. The median value is multiplied by the net sales
number of the main-company for the same year in question to provide
a second (or only) component in the CO2 estimate output by this
module 322C.
The estimate output by the module 322C using the median calculation
can be the average of the components mentioned above, or can be the
value of either component, but in either case is an estimated value
based on a median calculation of CO2 emission among the
comparison-class companies and the assumption that the
main-company's emission can be estimated in view of that
figure.
Next, module 322D includes instructions that implement a "policy
model" using heuristics configured to provide a value that is part
of the estimate for the total CO2 emissions of a company of
interest. The policy module obtains from the data source 420
information sufficient to answer the following policy-questions for
the main-company and for the comparison-class companies:
TABLE-US-00001 Parameter for Policy Question Algorithmic Processing
Does the company have a policy to improve En_En_RR_DP001[2] its
energy efficiency? Has there been a public commitment from a
En_En_RR_DP003[2] senior management or board member to energy
efficiency? Does the company describe, claim to have
En_En_RR_DP012[2] or mention processes in place to improve its
energy efficiency? Does the company claim to use key perfor-
En_En_RR_DP013[2] mance indicators (KPI) or the balanced scorecard
to monitor energy efficiency? Does the company show through the use
of En_En_RR_DP015[2] surveys or measurements that it is improving
its energy efficiency? Has the company set targets or objectives
En_En_RR_DP019[2] to be achieved on energy efficiency? Is the
company making progress or En_En_RR_DP020[2] succeeding to achieve
its previously set objectives on energy efficiency? Does the
company have a policy to En_En_ER_DP001[1] reduce emissions? Has
there been a public commitment from a En_En_ER_DP003[1] senior
management or board member to emission reduction? Does the company
describe, claim to have En_En_ER_DP005[1] or mention processes in
place to improve emission reduction? Does the company claim to use
key perfor- En_En_ER_DP010[1] mance indicators (KPI) or the
balanced scorecard to monitor emission reduction? Does the company
show through the use En_En_ER_DP012[1] of surveys or measurements
that it is reducing its emissions? Has the company set targets or
objectives En_En_ER_DP016[1] to be achieved on emission reduction?
Is the company making progress or En_En_ER_DP017[1] succeeding to
achieve its previously set objectives on emission reduction? Does
the company make direct use of En_En_RR_DP046 renewable energy
More specifically, the algorithm encoded in module 322D determines
how many of the policies noted above can be answered yes for the
main-company and for the comparison-class companies. Next, the
algorithm encoded in module 322D finds the percentile rank of the
main-company relative to the comparison-class companies using the
determination just made regarding the policies. If the main-company
is ranked less than the 25% percentile, then the module 322D
outputs a weighting or other factor to increase the estimate
produced by the "median model" of module 322C by 25%. On the other
hand, if the main-company is ranked greater than the 25%
percentile, then the module 322D outputs a weighting or other
factor to decrease the estimate produced by the "median model" of
module 322C by 25%.
Referring now to FIGS. 5A-5C, a portion of a user-interface 500
associated with the rating program 326 is illustrated as may be
seen on the user-machine 430 is illustrated in three parts for
discussion, though in an implementation the UI 500, each of FIGS.
5A-5C can be displayed in a common window. By interacting with the
user-interface 500, such as using a conventional mouse device or
other navigation tool, a user can manually drill-down to expose
factors that bear on a rating of the main-company such as by
click-selecting a control such as outcome collapse/expand control
510 (shown in the expanded state). In like manner, the user can
click-select to open and inspect drivers. For an explanation of
drivers and outcomes as they relate to rating companies, reference
is made to the aforementioned U.S. patent that has been
incorporated by reference. In FIG. 5, the outcomes that are
available for inspection are associated with the category of
"emission reduction" and are one of several categories under the
pillar of "environmental" factors. On the other hand, a user can
submit via a form 520 and search for a particular pillar, category,
driver, or outcome. Once a parameter of interest is selected, the
middle section of the UI 500 (FIG. 5B) shows the data points whose
values affect the main-company's rating. The right section of the
UI 500 (FIG. 5C) defines each data point and reveals the name of
the parameter used for algorithmic processing. Depending on the
implementation, the rating program or system may or may not permit
users to inspect down to the data point level. For instance,
permission to inspect and or change the weightings, parameters
relied upon or the algorithm itself can be based on whether the end
user is a subscriber to the system 300 or not. For more information
on access to multiple tiers of data, reference is made to U.S.
patent application Ser. No. 11/071,981, filed on Mar. 3, 2005,
entitled Tiered Access To Integrated Rating System, which is hereby
incorporated by reference as if set forth in its entirety
herein.
In FIG. 5, the user has entered greenhouse gases as the search term
in form 520 and that causes the emission reduction tab 530 to be
highlighted as well as the leaf 540 which is the specific outcome
parameter relating to emission reduction/greenhouse gas emissions.
FIG. 6, shows a detail page 600 of a user interface for a company
(British Petroleum PLC) including its rating 610, its standing
compared to a benchmark 620, and its rank 630. The data points 640
that govern the value of the parameter (in this example, a "B-"),
and the rules 650 that define the calculation of the greenhouse gas
emissions.
FIG. 7 illustrates further information that can be provided through
the UI, such as at page 600 by scrolling downward, and illustrates
the underlying source document information including a link (e.g.,
a URL or hypertext link) to the source of the particular data point
for that parameter, and also textual documentation in support of
the value of that parameter.
FIG. 8 illustrates the overall rating accorded to the category of
emission reduction (which in this example is an "A" rating), and
provides a breakdown of the ratings accorded to the drivers 820 and
outcomes 830. The outcome control 840 has been expanded to expose
the individual parameters such as greenhouse gas emissions 850, and
the rating for British Petroleum ("B-") is shown highlighted to the
user via the user interface.
In operation, code executing in the machine that comprises the
data-fault module 328 has access to the foregoing data and modules
322 (or to the output of the modules 322) and tests to determine if
there is a value available on the basis of reported data. For
instance, equation (1) below causes the reported value to be
displayed through a user interface on the user machine 430 if data
is available both from the records of the company under inspection
(e.g., British Petroleum) and if third-party reported data is
available and shows a non-zero sales figure. In that case, the
value is normalized to the sales figure. if
(isAvailable(@En_En_ER_DP023) and isAvailable(@Thomson_sales_USD)
and not @Thomson_sales_USD=0) then
(@En_En_ER_DP023/@Thomson_sales_USD) (1)
The data-fault module can output the reported value in this
instance. However, if the test performed is not satisfied, then the
data-fault module will further process the data, such as using
equation (2) below to cause a CO2 total emission estimate to be
populated in the database and presented through the user interface.
An exemplary fault condition can process the information as
follows: else if (isAvailable(@En_En_ER_DPNNN) and
isAvailable(@Thomson_sales_USD) and not @Thomson_sales_USD=0) then
(@En_En_ER_DPNNN/@Thomson_sales_USD) (2)
In some situations, however, there may not be reported value or
available third-party revenue information. In that circumstance,
further processing by the data-fault module can be by code
operative as follows: else NA (3)
The present invention addresses the risks that investors are
exposed to due to CO2 emissions by companies in their portfolios.
The modules described herein operate within a rating system or in
conjunction with a rating program 326 as in the aforementioned
patent to provide institutional investors with a tool through which
they can assess the carbon risk for companies within a portfolio,
even in circumstances in which some of the data is incomplete or
unreported. The modules use a diverse range of qualitative and
quantitative sector based factors to arrive at values that are
reliable and that can have any error associated with the individual
models accounted for. By correcting for such errors at the
model-level, the module(s) operate collectively to provide an
optimized routine for automated data estimation in the absence of
reported values. The estimates are fully transparent and enable
clients to customize and create their own estimate models.
While the invention has been described in connection with a certain
embodiment thereof, the invention is not limited to the described
embodiments but rather is more broadly defined by the recitations
in the claims below and equivalents thereof.
* * * * *